Image Embedding with Timm

author: Jael Gu, Filip

Desription

An image embedding operator generates a vector given an image. This operator extracts features for image with pretrained models provided by Timm. Timm is a deep-learning library developed by Ross Wightman, which maintains SOTA deep-learning models and tools in computer vision.

Code Example

Load an image from path './towhee.jpg' and use the pretrained ResNet50 model ('resnet50') to generate an image embedding.

Write the pipeline in simplified style:

import towhee

towhee.glob('./towhee.jpg') \
      .image_decode.cv2() \
      .image_embedding.timm(model_name='resnet50') \
      .show()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.glob['path']('./towhee.jpg') \
      .image_decode.cv2['path', 'img']() \
      .image_embedding.timm['img', 'vec'](model_name='resnet50') \
      .select('img', 'vec') \
      .show('img', 'vec')

Factory Constructor

Create the operator via the following factory method

image_embedding.timm(model_name='resnet34', num_classes=1000, skip_preprocess=False)

Parameters:

​ model_name: str

​ The model name in string. The default value is "resnet34". Refer Timm Docs to get a full list of supported models.

​ num_classes: int

​ The number of classes. The default value is 1000. It is related to model and dataset.

​ skip_preprocess: bool

​ The flag to control whether to skip image preprocess. The default value is False. If set to True, it will skip image preprocessing steps (transforms). In this case, input image data must be prepared in advance in order to properly fit the model.

Interface

An image embedding operator takes a towhee image as input. It uses the pre-trained model specified by model name to generate an image embedding in ndarray.

Parameters:

​ img: towhee.types.Image

​ The decoded image data in towhee.types.Image (numpy.ndarray).

Returns:

​ numpy.ndarray

​ The image embedding extracted by model.